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A fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach for suitable hotel recommendation amid the COVID-19 pandemic

Cities around the world have reopened from the lockdown caused by the COVID-19 pandemic, and more and more people are planning regional travel. Therefore, it is a practical problem to recommend suitable hotels to travelers amid the COVID-19 pandemic. However, it is also a challenging task since the...

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Autores principales: Toly Chen, Tin-Chih, Wu, Hsin-Chieh, Hsu, Keng-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907876/
https://www.ncbi.nlm.nih.gov/pubmed/35284086
http://dx.doi.org/10.1177/20552076221084457
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author Toly Chen, Tin-Chih
Wu, Hsin-Chieh
Hsu, Keng-Wei
author_facet Toly Chen, Tin-Chih
Wu, Hsin-Chieh
Hsu, Keng-Wei
author_sort Toly Chen, Tin-Chih
collection PubMed
description Cities around the world have reopened from the lockdown caused by the COVID-19 pandemic, and more and more people are planning regional travel. Therefore, it is a practical problem to recommend suitable hotels to travelers amid the COVID-19 pandemic. However, it is also a challenging task since the critical factors that affect hotel selection amid the COVID-19 pandemic may be different from those usually considered. From this perspective, the fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach is proposed in this study for hotel recommendation. The proposed methodology not only considers the critical factors affecting hotel selection amid the COVID-19 pandemic, but also establishes a systematic mechanism, that is, enhanced fuzzy geometric mean, to simultaneously improve the accuracy and efficiency of the recommendation process. The fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach has been successfully applied to recommend suitable hotels to 10 travelers for regional trips amid the COVID-19 pandemic.
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spelling pubmed-89078762022-03-11 A fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach for suitable hotel recommendation amid the COVID-19 pandemic Toly Chen, Tin-Chih Wu, Hsin-Chieh Hsu, Keng-Wei Digit Health Special Collection on Covid-19 Cities around the world have reopened from the lockdown caused by the COVID-19 pandemic, and more and more people are planning regional travel. Therefore, it is a practical problem to recommend suitable hotels to travelers amid the COVID-19 pandemic. However, it is also a challenging task since the critical factors that affect hotel selection amid the COVID-19 pandemic may be different from those usually considered. From this perspective, the fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach is proposed in this study for hotel recommendation. The proposed methodology not only considers the critical factors affecting hotel selection amid the COVID-19 pandemic, but also establishes a systematic mechanism, that is, enhanced fuzzy geometric mean, to simultaneously improve the accuracy and efficiency of the recommendation process. The fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach has been successfully applied to recommend suitable hotels to 10 travelers for regional trips amid the COVID-19 pandemic. SAGE Publications 2022-03-09 /pmc/articles/PMC8907876/ /pubmed/35284086 http://dx.doi.org/10.1177/20552076221084457 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Special Collection on Covid-19
Toly Chen, Tin-Chih
Wu, Hsin-Chieh
Hsu, Keng-Wei
A fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach for suitable hotel recommendation amid the COVID-19 pandemic
title A fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach for suitable hotel recommendation amid the COVID-19 pandemic
title_full A fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach for suitable hotel recommendation amid the COVID-19 pandemic
title_fullStr A fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach for suitable hotel recommendation amid the COVID-19 pandemic
title_full_unstemmed A fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach for suitable hotel recommendation amid the COVID-19 pandemic
title_short A fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach for suitable hotel recommendation amid the COVID-19 pandemic
title_sort fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach for suitable hotel recommendation amid the covid-19 pandemic
topic Special Collection on Covid-19
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907876/
https://www.ncbi.nlm.nih.gov/pubmed/35284086
http://dx.doi.org/10.1177/20552076221084457
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